Long-term hospital management in the presence of COVID-19: A practical perspective
Bibliographic record
Abstract
In December 2019, a novel pneumonia caused by a previously unknown pathogen emerged in Wuhan, China. Whereas, thus far, the large majority of people infected by SARS-CoV-2 develop mild inconsequential respiratory symptoms, a minority of mostly fragile, immunecompromised, often aged individuals with chronic medical conditions, develop a severe form of acute respiratory distress syndrome (ARDS) and shock leading to death. Thanks to the early implementation of a social distancing strategy, some regions have seen only a moderate but significant increase in the number of SARS-CoV-2 infection. Although, a significant increase in severe and critical COVID-19 patients was noted, requiring significant investment in dedicated personnel and allocation of specific hospitalization and intensive care unit (ICU) infrastructure and resources, but the medical systems’ functioning was not completely disrupted. As the development of a readily available vaccine against the new coronavirus is expected to take about 1.5 - 2 years, most hospitals will have to address the problems and challenges of caring for regular patients, some of them high-risk patients for SARS-CoV-2 infection, while caring in parallel for a low to moderate number of COVID-19 infected patients. This report presents an outline for a plan of action of a hospital system to deal with such an eventuality. We review the key changes that must be implemented in hospital management and activity to prevent disruption of key services due to the COVID-19 outbreak and the maintenance of high quality of care to all patients while ensuring the highest standards of staff and patient safety.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".